Web-based Graphical Interface Research Articles

Versatile multiple object tracking in sparse 2D/3D videos via deformable image registration.

Tracking body parts in behaving animals, extracting fluorescence signals from cells embedded in deforming tissue, and analyzing cell migration patterns during development all require tracking objects with partially correlated motion. As dataset sizes increase, manual tracking of objects becomes prohibitively inefficient and slow, necessitating automated and semi-automated computational tools. Unfortunately, existing methods for multiple object tracking (MOT) are either developed for specific datasets and hence do not generalize well to other datasets, or require large amounts of training data that are not readily available. This is further exacerbated when tracking fluorescent sources in moving and deforming tissues, where the lack of unique features and sparsely populated images create a challenging environment, especially for modern deep learning techniques. By leveraging technology recently developed for spatial transformer networks, we propose ZephIR, an image registration framework for semi-supervised MOT in 2D and 3D videos. ZephIR can generalize to a wide range of biological systems by incorporating adjustable parameters that encode spatial (sparsity, texture, rigidity) and temporal priors of a given data class. We demonstrate the accuracy and versatility of our approach in a variety of applications, including tracking the body parts of a behaving mouse and neurons in the brain of a freely moving C. elegans. We provide an open-source package along with a web-based graphical user interface that allows users to provide small numbers of annotations to interactively improve tracking results.

A Wireless Noninvasive Blood Pressure Measurement System Using MAX30102 and Random Forest Regressor for Photoplethysmography Signals

Hypertension, often termed “the silent killer”, is associated with cardiovascular risk and requires regular blood pressure (BP) monitoring. However, existing methods are cumbersome and require medical expertise, which is worsened by the need for physical contact, particularly during situations such as the coronavirus pandemic that started in 2019 (COVID-19). This study aimed to develop a cuffless, continuous, and accurate BP measurement system using a photoplethysmography (PPG) sensor and a microcontroller via PPG signals. The system utilizes a MAX30102 sensor and ESP-WROOM-32 microcontroller to capture PPG signals that undergo noise reduction during preprocessing. Peak detection and feature extraction algorithms were introduced, and their output data were used to train a machine learning model for BP prediction. Tuning the model resulted in identifying the best-performing model when using a dataset from six subjects with a total of 114 records, thereby achieving a coefficient of determination of 0.37/0.46 and a mean absolute error value of 4.38/4.49 using the random forest algorithm. Integrating this model into a web-based graphical user interface enables its implementation. One probable limitation arises from the small sample size (six participants) of healthy young individuals under seated conditions, thereby potentially hindering the proposed model’s ability to learn and generalize patterns effectively. Increasing the number of participants with diverse ages and medical histories can enhance the accuracy of the proposed model. Nevertheless, this innovative device successfully addresses the need for convenient, remote BP monitoring, particularly during situations like the COVID-19 pandemic, thus making it a promising tool for cardiovascular health management.

Distance-based novelty detection model for identifying individuals at risk of developing Alzheimer's disease.

Novelty detection (ND, also known as one-class classification) is a machine learning technique used to identify patterns that are typical of the majority class and can discriminate deviations as novelties. In the context of Alzheimer's disease (AD), ND could be employed to detect abnormal or atypical behavior that may indicate early signs of cognitive decline or the presence of the disease. To date, few research studies have used ND to discriminate the risk of developing AD and mild cognitive impairment (MCI) from healthy controls (HC). In this work, two distinct cohorts with highly heterogeneous data, derived from the Australian Imaging Biomarkers and Lifestyle (AIBL) Flagship Study of Ageing project and the Fujian Medical University Union Hospital (FMUUH) China, were employed. An innovative framework with built-in easily interpretable ND models constructed solely on HC data was introduced along with proposing a strategy of distance to boundary (DtB) to detect MCI and AD. Subsequently, a web-based graphical user interface (GUI) that incorporates the proposed framework was developed for non-technical stakeholders. Our experimental results indicate that the best overall performance of detecting AD individuals in AIBL and FMUUH datasets was obtained by using the Mixture of Gaussian-based ND algorithm applied to single modality, with an AUC of 0.8757 and 0.9443, a sensitivity of 96.79% and 89.09%, and a specificity of 89.63% and 90.92%, respectively. The GUI offers an interactive platform to aid stakeholders in making diagnoses of MCI and AD, enabling streamlined decision-making processes. More importantly, the proposed DtB strategy could visually and quantitatively identify individuals at risk of developing AD.

A service-oriented orchestration and planning tool for plug and produce manufacturing: a deformable object handling supervision paradigm

ABSTRACT Recurrent production changes due to shortened product lifecycles besides the increased complexity, that the industry 4.0 paradigm brings, highlight the need for tools that are able to seamlessly orchestrate intelligent resource agents and guarantee rapid commissioning times. This paper presents a complete package for the creation, debugging, editing, management and execution of complex work schedules of robotic systems, named Workcell Controller. The controller is capable of simultaneously orchestrating multiple resources and modules or managing errors based on the real-time shopfloor’s state. For greater applicability and compatibility, it is completely integrated with the ROS framework, meaning that standard ROS interfaces, functionalities and practices can be deployed. An intuitive web-based graphical user interface is designed for offering high usability even for novice users during programming and execution phases. The proposed package has been deployed and tested in two use cases inspired from the composites industry, demonstrating the controller’s applicability, usability and short ramp up times.

Machine learning-based prediction and experimental validation of heavy metal adsorption capacity of bentonite

As a natural adsorbent material, bentonite is widely used in the field of heavy metal adsorption. The heavy metal adsorption capacity of bentonite varies significantly in studies due to the differences in the properties of bentonite, solution, and heavy metal. To achieve accurate predictions of bentonite's heavy metal adsorption capacity, this study employed six machine learning (ML) regression algorithms to investigate the adsorption characteristics of bentonite. Finally, an eXtreme Gradient Boosting Regression (XGB) model with outstanding predictive performance was constructed. Explanation analysis of the XGB model further reveal the importance and influence manner of each input feature in predicting the heavy metal adsorption capacity of bentonite. The feature categories influencing heavy metal adsorption capacity were ranked in order of importance as adsorption conditions > bentonite properties > heavy metal properties. Furthermore, a web-based graphical user interface (GUI) software was developed, facilitating researchers and engineers to conveniently use the XGB model for predicting the heavy metal adsorption capacity of bentonite. This study provides new insights into the adsorption behaviors of bentonite for heavy metals, offering guidance and support for enhancing its application efficiency and addressing heavy metal pollution remediation.

A Wireless Data Acquisition System Based on MEMS Accelerometers for Operational Modal Analysis of Bridges.

This paper illustrates a novel and cost-effective wireless monitoring system specifically developed for operational modal analysis of bridges. The system employs battery-powered wireless sensors based on MEMS accelerometers that dynamically balance power consumption with high processing features and a low-power, low-cost Wi-Fi module that ensures operation for at least five years. The paper focuses on the system's characteristics, stressing the challenges of wireless communication, such as data preprocessing, synchronization, system lifetime, and simple configurability, achieved through the integration of a user-friendly, web-based graphical user interface. The system's performance is validated by a lateral excitation test of a model structure, employing dynamic identification techniques, further verified through FEM modeling. Later, a system composed of 30 sensors was installed on a concrete arch bridge for continuous OMA to assess its behavior. Furthermore, emphasizing its versatility and effectiveness, displacement is estimated by employing conventional and an alternative strategy based on the Kalman filter.

Software Quality Assurance as a Service: Encompassing the quality assessment of software and services

This paper introduces the Software Quality Assurance as a Service (SQAaaS) concept and it describes an open-source implementation of a comprehensive platform that supports the automated assessment of specific quality metrics for software and services, defined as a set of baseline requirements. The platform is openly accessible, focuses on research software and open science, and promotes best practices by awarding standards-based digital badges to software and services. It provides an easy-to-use web-based graphical user interface which facilitates the interaction with server-side components in charge of automatically creating CI/CD (Continuous Integration/Continuous Delivery) pipelines for automated testing of the baseline criteria. The service is in production and has performed over 2800 assessments, awarding more than 125 digital badges across several scientific disciplines.

Visualizing Clinical Data Retrieval and Curation in Multimodal Healthcare AI Research: A Technical Note on RIL-workflow.

Curating and integrating data from sources are bottlenecks to procuring robust training datasets for artificial intelligence (AI) models in healthcare. While numerous applications can process discrete types of clinical data, it is still time-consuming to integrate heterogenous data types. Therefore, there exists a need for more efficient retrieval and storage of curated patient data from dissimilar sources, such as biobanks, health records, and sensors. We describe a customizable, modular data retrieval application (RIL-workflow), which integrates clinical notes, images, and prescription data, and show its feasibility applied to research at our institution. It uses the workflow automation platform Camunda (Camunda Services GmbH, Berlin, Germany) to collect internal data from Fast Healthcare Interoperability Resources (FHIR) and Digital Imaging and Communications in Medicine (DICOM) sources. Using the web-based graphical user interface (GUI), the workflow runs tasks to completion according to visual representation, retrieving and storing results for patients meeting study inclusion criteria while segregating errors for human review. We showcase RIL-workflow with its library of ready-to-use modules, enabling researchers to specify human input or automation at fixed steps. We validated our workflow by demonstrating its capability to aggregate, curate, and handle errors related to data from multiple sources to generate a multimodal database for clinical AI research. Further, we solicited user feedback to highlight the pros and cons associated with RIL-workflow. The source code is available at github.com/magnooj/RIL-workflow.

Finback: a web-based data collection system at SSRF biological macromolecular crystallography beamlines.

An integrated computer software system for macromolecular crystallography (MX) data collection at the BL02U1 and BL10U2 beamlines of the Shanghai Synchrotron Radiation Facility is described. The system, Finback, implements a set of features designed for the automated MX beamlines, and is marked with auser-friendly web-based graphical user interface (GUI) for interactive data collection. The Finback client GUI can run on modern browsers and has been developed using several modern web technologies including WebSocket, WebGL, WebWorker and WebAssembly. Finback supports multiple concurrent sessions, so on-site and remote users can access the beamline simultaneously. Finback also cooperates with the deployed experimental data and information management system, the relevant experimental parameters and results are automatically deposited to a database.

Web-based visualization and rendering of aerial LiDAR point cloud for urban flood simulation

Purpose In an era overshadowed by the alarming consequences of climate change and the escalating peril of recurring floods for communities worldwide, the significance of proficient disaster risk management has reached unprecedented levels. The successful implementation of disaster risk management necessitates the ability to make informed decisions. To this end, the utilization of three-dimensional (3D) visualization and Web-based rendering offers decision-makers the opportunity to engage with interactive data representations. This study aims to focus on Thiruvananthapuram, India, where the analysis of flooding caused by the Karamana River aims to furnish valuable insights for facilitating well-informed decision-making in the realm of disaster management. Design/methodology/approach This work introduces a systematic procedure for evaluating the influence of flooding on 3D building models through the utilization of Web-based visualization and rendering techniques. To ensure precision, aerial light detection and ranging (LiDAR) data is used to generate accurate 3D building models in CityGML format, adhering to the standards set by the Open Geospatial Consortium. By using one-meter digital elevation models derived from LiDAR data, flood simulations are conducted to analyze flow patterns at different discharge levels. The integration of 3D building maps with geographic information system (GIS)-based vector maps and a flood risk map enables the assessment of the extent of inundation. To facilitate visualization and querying tasks, a Web-based graphical user interface (GUI) is developed. Findings The efficiency of comprehensive 3D building maps in evaluating flood consequences in Thiruvananthapuram has been established by the research. By merging with GIS-based vector maps and a flood risk map, it becomes possible to scrutinize the extent of inundation and the affected structures. Furthermore, the Web-based GUI facilitates interactive data exploration, visualization and querying, thereby assisting in decision-making. Originality/value The study introduces an innovative approach that merges LiDAR data, 3D building mapping, flood simulation and Web-based visualization, which can be advantageous for decision-makers in disaster risk management and may have practical use in various regions and urban areas.

AutoBPS-Prototype: A web-based toolkit to automatically generate prototype building energy models with customizable efficiency values in China

Prototype building energy models are significant in building energy performance evaluation and energy efficiency potential analysis. However, their establishment process is time-consuming and error-prone. This study aims to develop a user-friendly toolkit, AutoBPS-Prototype, to automatically establish prototype building energy models in China. A web-based graphical user interface of AutoBPS-Prototype was designed and implemented. AutoBPS-Prototype only needs to define the climate zone, building type, and standard vintage to generate a default prototype building energy model based on a pre-built library. In addition, it can generate user-defined models through customized important efficiency values. The OpenStudio Standards used to create the U.S. Department of Energy prototype building models based on the ASHRAE 90.1 standards was extended to cover more prototype building energy models in China, including 20 sub-climate zones, 22 building types and four code and standard vintages. The Chinese building standards were used for the efficiency settings. Besides, case studies were conducted. Firstly, 20 prototype building models of four building types in five cities were generated. The credibility of the model results simulated by EnergyPlus was verified through comparison with measured data. Then, the impact of spatiotemporal differences on building energy consumption was analyzed through simulation of the models using different standard vintages in different climate zones. Finally, a baseline and four upgrade scenarios were designed to explore the impact of various upgrades on building energy consumption. This toolkit aims to assist users in conducting rapid research on building energy efficiency improvement. More toolkit features are under development for retrofit analysis, demand response analysis, and photovoltaics system generation estimation.

A new paradigm for molecular dynamics databases: the COVID-19 database, the legacy of a titanic community effort.

Molecular dynamics (MD) simulations are keeping computers busy around the world, generating a huge amount of data that is typically not open to the scientific community. Pioneering efforts to ensure the safety and reusability of MD data have been based on the use of simple databases providing a limited set of standard analyses on single-short trajectories. Despite their value, these databases do not offer a true solution for the current community of MD users, who want a flexible analysis pipeline and the possibility to address huge non-Markovian ensembles of large systems. Here we present a new paradigm for MD databases, resilient to large systems and long trajectories, and designed to be compatible with modern MD simulations. The data are offered to the community through a web-based graphical user interface (GUI), implemented with state-of-the-art technology, which incorporates system-specific analysis designed by the trajectory providers. A REST API and associated Jupyter Notebooks are integrated into the platform, allowing fully customized meta-analysis by final users. The new technology is illustrated using a collection of trajectories obtained by the community in the context of the effort to fight the COVID-19 pandemic. The server is accessible at https://bioexcel-cv19.bsc.es/#/. It is free and open to all users and there are no login requirements. It is also integrated into the simulations section of the BioExcel-MolSSI COVID-19 Molecular Structure and Therapeutics Hub: https://covid.molssi.org/simulations/ and is part of the MDDB effort (https://mddbr.eu).

Spectroscape enables real-time query and visualization of a spectral archive in proteomics

In proteomics, spectral archives organize the enormous amounts of publicly available peptide tandem mass spectra by similarity, offering opportunities for error correction and novel discoveries. Here we adapt an indexing algorithm developed by Facebook for organizing online multimedia resources to tandem mass spectra and achieve practically instantaneous retrieval and clustering of approximate nearest neighbors in a large spectral archive. An interactive web-based graphical user interface enables the user to view a query spectrum in its clustered neighborhood, which facilitates contextual validation of peptide identifications and exploration of the dark proteome.

DTAmetasa: An R shiny application for meta-analysis of diagnostic test accuracy and sensitivity analysis of publication bias.

Meta-analysis of diagnostic test accuracy (DTA) is a powerful statistical method for synthesizing and evaluating the diagnostic capacity of medical tests and has been extensively used by clinical physicians and healthcare decision-makers. However, publication bias (PB) threatens the validity of meta-analysis of DTA. Some statistical methods have been developed to deal with PB in meta-analysis of DTA, but implementing these methods requires high-level statistical knowledge and programming skill. To assist non-technical users in running most routines in meta-analysis of DTA and handling with PB, we developed an interactive application, DTAmetasa. DTAmetasa is developed as a web-based graphical user interface based on the R shiny framework. It allows users to upload data and conduct meta-analysis of DTA by "point and click" operations. Moreover, DTAmetasa provides the sensitivity analysis of PB and presents the graphical results to evaluate the magnitude of the PB under various publication mechanisms. In this study, we introduce the functionalities of DTAmetasa and use the real-world meta-analysis to show its capacity for dealing with PB.

UmetaFlow: an untargeted metabolomics workflow for high-throughput data processing and analysis

Metabolomics experiments generate highly complex datasets, which are time and work-intensive, sometimes even error-prone if inspected manually. Therefore, new methods for automated, fast, reproducible, and accurate data processing and dereplication are required. Here, we present UmetaFlow, a computational workflow for untargeted metabolomics that combines algorithms for data pre-processing, spectral matching, molecular formula and structural predictions, and an integration to the GNPS workflows Feature-Based Molecular Networking and Ion Identity Molecular Networking for downstream analysis. UmetaFlow is implemented as a Snakemake workflow, making it easy to use, scalable, and reproducible. For more interactive computing, visualization, as well as development, the workflow is also implemented in Jupyter notebooks using the Python programming language and a set of Python bindings to the OpenMS algorithms (pyOpenMS). Finally, UmetaFlow is also offered as a web-based Graphical User Interface for parameter optimization and processing of smaller-sized datasets. UmetaFlow was validated with in-house LC–MS/MS datasets of actinomycetes producing known secondary metabolites, as well as commercial standards, and it detected all expected features and accurately annotated 76% of the molecular formulas and 65% of the structures. As a more generic validation, the publicly available MTBLS733 and MTBLS736 datasets were used for benchmarking, and UmetaFlow detected more than 90% of all ground truth features and performed exceptionally well in quantification and discriminating marker selection. We anticipate that UmetaFlow will provide a useful platform for the interpretation of large metabolomics datasets.Graphical

RACS2: a framework of a remote autonomous control system for telescope observation and its application

With the increasing demand for astronomical observations, telescope systems are becoming increasingly complex. Thus, the observatory control software needs to be more intelligent. It has to control each instrument inside the observatory, finish the observational tasks autonomously, and report the information to users if needed. We developed a distributed autonomous observatory control framework named the Remote Autonomous Control System 2nd (RACS2) to meet these requirements. Rich features are integrated into the RACS2 framework. The RACS2 is a modular framework, in which each device control software and system services are implemented as different components. Furthermore, the RACS2 framework assimilates new techniques, such as a lightweight message and serialization mechanism. RACS2 also has good compatibility with other frameworks or ecosystems such as Python and the EPICS (Experimental Physics and Industrial Control System). The RACS2 framework can communicate with EPICS-based and Python-based software. With the help of these features, key system components like executor, scheduler are developed. The executor component can support sophisticated tasks. Autonomous observation can be achieved by the scheduler component. A set of web-based graphical user interface (GUI) is designed to help control and manage the framework remotely. Based on the RACS2 framework, we have implemented the Dome A Twins (DATs) telescope’s observation system and the space object observation system. The systems have been operated for more than 1 year. The RACS2 framework also has been used in our other projects like camera control system of Wide Field Survey Telescope (WFST) and its observatory control system, and the design can be a useful reference for the development of other frameworks.

The personalized cancer network explorer (PeCaX) as a visual analytics tool to support molecular tumor boards

BackgroundPersonalized oncology represents a shift in cancer treatment from conventional methods to target specific therapies where the decisions are made based on the patient specific tumor profile. Selection of the optimal therapy relies on a complex interdisciplinary analysis and interpretation of these variants by experts in molecular tumor boards. With up to hundreds of somatic variants identified in a tumor, this process requires visual analytics tools to guide and accelerate the annotation process.ResultsThe Personal Cancer Network Explorer (PeCaX) is a visual analytics tool supporting the efficient annotation, navigation, and interpretation of somatic genomic variants through functional annotation, drug target annotation, and visual interpretation within the context of biological networks. Starting with somatic variants in a VCF file, PeCaX enables users to explore these variants through a web-based graphical user interface. The most protruding feature of PeCaX is the combination of clinical variant annotation and gene-drug networks with an interactive visualization. This reduces the time and effort the user needs to invest to get to a treatment suggestion and helps to generate new hypotheses. PeCaX is being provided as a platform-independent containerized software package for local or institution-wide deployment. PeCaX is available for download at https://github.com/KohlbacherLab/PeCaX-docker.

An IoT-based energy management system for AC microgrids with grid and security constraints

This paper proposes an Internet-of-Things (IoT) based energy management system (EMS) for the optimal operation of unbalanced three-phase AC microgrids. The system utilizes a software architecture based on microservices, which includes a stochastic economic dispatch optimizer (EDO), a database, a web-based graphical user interface (GUI), and an application programming interface (API). The EDO uses a mixed-integer linear programming (MILP) model to ensure the day-ahead dispatch of the distributed energy resources (DERs) in the microgrid while adhering to grid constraints such as voltage, current, and power limits. Additionally, the optimization module takes into account security constraints for unplanned islanded operation, as well as stochastic scenarios of local demand and renewable generation. To assess the performance of the proposed IoT-based EMS, tests are conducted using a real-time simulator in a software-in-the-loop (SIL) experimental setup. Actual data from a microgrid located at the State University of Campinas (UNICAMP) in Brazil is utilized for the tests. The microgrid consisted of a photovoltaic (PV) system, a battery energy storage system (BESS), a thermal generation unit, and variable demands. Results indicated the effectiveness of the proposed IoT-based EMS in monitoring the operation of the microgrid and defining the optimal day-ahead dispatch of local DERs.

Crop Yield Prediction Using Machine Learning

The majority of India’s agricultural products have been negatively impacted by climate change in terms of performance over the past 20 years. Prior to harvest, crop output predictions would aid farmers and policymakers in deciding on the best course of action for marketing and storage. Before cultivating on the agricultural field, this project will assist the farmers in learning the yield of their crop, enabling them to make the best choices. By creating a working prototype of an interactive prediction system, it tries to find a solution. It will be put into practise to implement such a system with a user-friendly web-based graphic user interface and the machine learning algorithm. The farmer will have access to the prediction’s outcomes. So, there are various ways or algorithms for this type of data analytics in crop prediction, and we can anticipate crop production with the aid of those algorithms. It employs the random forest algorithm. There are no suitable technologies or solutions to deal with the scenario we are in, despite the analysis of all these concerns and problems, including weather, temperature, humidity, rainfall, and moisture. In India, there are numerous ways to boost agricultural economic development. Data mining can be used to forecast crop yield growth. Data mining is, in general, the process of reviewing data from various angles and distilling it into pertinent information. The most well-known and effective supervised machine learning algorithm, random forest, can perform both classification and regression tasks. It works by building a large number of decision trees during training time and producing output of the class that is the mean prediction (for regression) or mode of the classes (for classification) of the individual trees.

Crop Yield Prediction Using Machine Learning

The majority of India’s agricultural products have been negatively impacted by climate change in terms of performance over the past 20 years. Prior to harvest, crop output predictions would aid farmers and policymakers in deciding on the best course of action for marketing and storage. Before cultivating on the agricultural field, this project will assist the farmers in learning the yield of their crop, enabling them to make the best choices. By creating a working prototype of an interactive prediction system, it tries to find a solution. It will be put into practise to implement such a system with a user-friendly web-based graphic user interface and the machine learning algorithm. The farmer will have access to the prediction’s outcomes. So, there are various ways or algorithms for this type of data analytics in crop prediction, and we can anticipate crop production with the aid of those algorithms. It employs the random forest algorithm. There are no suitable technologies or solutions to deal with the scenario we are in, despite the analysis of all these concerns and problems, including weather, temperature, humidity, rainfall, and moisture. In India, there are numerous ways to boost agricultural economic development. Data mining can be used to forecast crop yield growth. Data mining is, in general, the process of reviewing data from various angles and distilling it into pertinent information. The most well-known and effective supervised machine learning algorithm, random forest, can perform both classification and regression tasks. It works by building a large number of decision trees during training time and producing output of the class that is the mean prediction (for regression) or mode of the classes (for classification) of the individual trees.